Gesture-control systems provide simple and intuitional convenient operation. However, the systems using touch-controlled human-machine interfaces such as touch panels require users to perform operation by directly contacting the interfaces, thus being inconvenient to some applications. Contrary, the gesture-control systems using non-touch interfaces allows users to perform operation at a relatively distant place therefrom, while being more difficult to implement because such systems determine gestures by capturing and identifying images. Currently, the methods for image-based gesture identification can be classified into two categories, one using natural images without auxiliary illuminant, while the other using unnatural images generated by one or more auxiliary light sources.
Compared to the gesture-control systems using auxiliary illuminant, the gesture-control systems not using auxiliary illuminant are more advantageous because they require lower costs, can be combined with a camera, and save power, while having the innate weakness of more difficulty in gesture identification. The methods for gesture identification employed by a gesture-control system not using auxiliary illuminant are typically established on either motion estimation or shape detection. Since gestures and operational habits are usually different among users, gesture identification using motion estimation is particularly weak in identifying certain gestures, such as click and zoom in/out that include Z-axis motions. On the other hand, gesture identification using shape detection usually requires users to operate with certain gestures that are fixed and recognizable to the system, for example, making a fist or opening a palm. FIG. 1 and FIG. 2 illustrate a conventional method for gesture identification using shape detection, in which a camera module 10 captures images at a constant frame rate. If a user waves his/her palm horizontally, namely along the X axis or the Y axis, before the camera module 10, the camera module 10 will obtain successive images, such as f(1) and f(2) shown in FIG. 2, in which the positions of the profiles 14 and 16 of the hand 12 in the two images f(1) and f(2) are different. The system first identifies a certain portion of the profiles that has a predetermined shape from the images f(1) and f(2), for example, the fingertips 18 and 20 of the index finger, and then uses the positional difference between the fingertip images 18 and 20 in the images f(1) and f(2) to identify that the gesture is one performing a rightward wave. This conventional method requires high-definition images for correct recognition of the image with the predetermined shape, and is not adaptive to indistinct images caused by fast motions of an object, thus being unsuitable for applications related to short distance. Furthermore, if the user changes his/her hand posture in the course of operation, the system may fail to recognize the image of the predetermined shape and become unable to identify the gesture. Since the recognizable images are limited to those of certain shapes, some gestures are not definable and this greatly restricts the scope of operational gestures. In general, only those gestures displaying apparent characteristics can be predefined in the system. Additionally, since significant variation exists between the images of a user's hand when the hand moves away from or toward the camera module 10, the conventional method is not supportive to gestures performing Z-axis (i.e. vertical) motions.
The method for gesture identification using shape detection includes recognizing the region of skin color in an image, then identifying the shape of the recognized region of skin color, and finally finding out where a hand is in the image. However, skin-color analysis requires complex algorithm and is color temperature dependent, so the error rate is relatively high. Shape recognition also needs complex algorithm. These processes need numerous arithmetic operations, and thus require higher costs for both software and hardware, and slower down the system's response.